Gravitational wave spectroscopy of binary neutron star merger remnants with mode stacking
Bibliographic record
Abstract
A binary neutron star (BNS) merger event has recently been observed in gravitational waves (GWs). As in the case of binary black holes, GWs generated by BNS consist of inspiral, merger, and post-merger components. Detecting the latter is important because it encodes information about the nuclear equation of state (EOS) in a regime that cannot be probed prior to merger. The post-merger signal, however, can be observed by current detectors only out to~ 10 Mpc. We carry out Monte-Carlo simulations showing that the dominant post-merger signal (the 22 mode) from individual events will likely not be observable even with the Einstein Telescope and Cosmic Explorer (CE), assuming a full year of operation, the latest merger rates, and a detection threshold with signal-to-noise ratio of 5. For this reason, we propose two methods that stack the post-merger signal from multiple events to boost the detection probability. The first method follows a commonly-used practice of multiplying the Bayes factors of individual events. The second method relies on an assumption that the mode phase can be determined from the inspiral waveform, so that coherent mode stacking of the data from different events becomes possible. Both methods significantly improve the chances of detecting the dominant post-merger signal, making a detection very likely after a year of observation with CE for certain EOS. We also show that in terms of detection, coherent stacking is more efficient in accumulating confidence. Moreover, assuming a 22 mode is detected with CE via stacking, we estimate through a Fisher analysis that the peak frequency can be measured to a statistical error of ~ 4-20 Hz for certain equations of state. Such an error corresponds to a NS radius measurement of ~ 15-56 m, a fractional relative error ~ 4 %, suggesting that systematic errors from theoretical modeling (~ 100 m) may dominate the error budget.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".